Senior Data Engineer

Role: Sr/ Lead Databricks Developer (Azure, AWS) Responsibilities

• Closely work with Architect and lead to design solutions to meet functional and non-functional requirements.
• Participate to understand architecture and solution design artifacts.
• Evangelize re-use through the implementation of shared assets.
• Proactively implement engineering methodologies, standards, and leading practices.
• Provide insight and direction on roles and responsibilities required for solution operations.
• Identify, communicate and mitigate Risks, Assumptions, Issues, and Decisions throughout the full lifecycle.
• Considers the art of the possible, compares various solution options based on feasibility and impact, and proposes actionable plans.
• Demonstrate strong analytical and technical problem-solving skills.
• Ability to analyze and operate at various levels of abstraction.
• Ability to balance what is strategically right with what is practically realistic.

• Must have excellent coding skills either Python or Scala, preferably Python.
• Must have at least 5+ years of experience in Data Engineering domain with total of 7+ years.
• Must have implemented at least 2 project end-to-end in Databricks.
• Must have at least 2+ years of experience on databricks which consists of various components as below
• Delta lake
• dbConnect
• db API 2.0
• Databricks workflows orchestration
• Must be well versed with Databricks Lakehouse concept and its implementation in enterprise environments.
• Must have strong understanding of Data warehousing and various governance and security standards around Databricks.
• Must have knowledge of cluster optimization and its integration with various cloud services.
• Must have good understanding to create complex data pipeline.
• Must have good knowledge of Data structure & algorithms.
• Must be strong in SQL and sprak-sql.
• Must have strong performance optimization skills to improve efficiency and reduce cost.
• Must have worked on both Batch and streaming data pipeline.
• Must have extensive knowledge of Spark and Hive data processing framework.
• Must have worked on any cloud (Azure, AWS, GCP) and most common services like ADLS/S3, ADF/Lambda, CosmosDB/DynamoDB, ASB/SQS, Cloud databases.
• Must be strong in writing unit test case and integration test.